A Prediction Model for Screening Covid-19 Patients

ABSTRACT

This study aims to compare the performance of data classifying for COVID-19 patients. In this study, the patients' data acquired from the department of disease control (1,608,923 patients) are collected. They are patients records from January 2020 to October 2021. The study focus on three main data classification techniques Random forest;Neural Network;and Naïve Bayes. The authors study the comparative performance of the techniques. We apply the split test method to evaluate the performance of data prediction. The data are divided into two parts training data. The results show that Random Forest has an accuracy of 93.51%. Neural network has an accuracy of 93.02%. Naive Bayes has an accuracy of 27.54%. This presents the Random Forest with the highest accuracy Figure for screening of COVID-19 patients © 2022 IEEE.